Structural Phrase Alignment Based on Consistency Criteria
Toshiaki Nakazawa

Yu KunSadao Kurohashi

Graduate School of Informatics, Kyoto University
Yoshida-honmachi, Sakyo-ku
Kyoto, 606-8501, Japan{nakazawa, kunyu}@nlp.kuee.kyoto-u.ac.jp

kuro@i.kyoto-u.ac.jp

Abstract
In this paper, we propose a new method for phrase alignment using a dependency type distance and a distance-score function. With thismethod, appropriate correspondences can be selected among correspondence candidates that often include ambiguous or incorrect ones.
Furthermore, this method makes it possible to measure the overall alignment consistency. We conduct an alignment experiment using
500 parallel sentences on newspaper domain, and achieve an F-measure improvement of 35 points over the simple statistical method(GIZA++), and 3.0 points over a baseline system. We also conducted a translation experiment and achieved a BLEU score improvementof 0.4 points over a baseline system.

1

Introduction

In machine translation task, how to align the training parallel corpus with high accuracy is a big problem, and thus
a number of studies have been done. The alignment methods can be categorized into two groups: one is probabilistic
method and the other is heuristic method with structural information.
Probabilistic methods are mainly used in Statistical Machine Translation (SMT) systems (Och and Ney, 2003a).
The main issue is how to decompose the alignment probabilities P r(A|S, T) reasonably to make good use of someapproximations.
The simplest statistical method is based on word levelalignment, in which the IBM Model (Brown et al., 1993)
is mostly used as the baseline method. Recently, more sophisticated methods have been proposed by (Watanabe etal., 2002) and (Zhang and Vogel, 2005), which handle not
only a word but a larger block which is usually a multiple word or a phrase. However, even if these methods areoriented to use larger block or structure, data sparseness isstill a big problem on its way. For this reason, it is not easy
to achieve high performance for the language pair whose
linguistic structure is quite different from each other.
While, by using heuristic rules in alignment procedure,
structural methods can easily use NLP resources, such as amorphological analyzer and a syntactic analyzer, to grasp
characteristics of language pairs with large difference inlinguistic structure.
(Menezes and Richardson, 2001) proposed a kind oftree structure called “Logical Form”, which is a disorderedgraph representing the relations among the most meaningful elements of a sentence. With this structure, they proposed a “best-first” alignment method. This method starts
from the nodes with the tightest lexical correspondence andthen goes to close nodes from the first node. (Groves etal., 2004) used parsed tree structure of an original sentence,and then aligned the trees with some heuristic rules that
constrain the order of alignment.
Although these structural methods utilize profoundknowledge of NLP and achieve high accuracy, the mannerof alignment is still heuristic, which is often not in general
purpose. To resolve this issue, (Gildea, 2003) proposed aprobabilistic tree-based alignment between Korean and En-

glish. They use some cloning operations to calculate theprobability, so they make the structure more complicated.
Moreover, it is not apparent that the same operations are
effective and suitable for different language pairs.
In this paper, we propose an alignment method applying dependency type distance and distance score function
into the structural alignment. Our motivation is to measure the alignment consistency based on distance, which
is not only keeping the simple sentence structure but also
language independent. Experimental result shows our proposed method can achieve about 3 points improvement onalignment accuracy.
In the following section, we briefly introduce the basic
structural alignment module in our machine translation system. In Section 3, our proposed methods are introduced:
there are three methods: baseline, model 1, and model 2.
We performed some experiments to evaluate our proposal,
and it is reported in Section 4. At last, we give a short conclusion and introduce our future work.

2 Procedure of Structural Phrase Alignment
Our machine translation system works mainly for JapaneseEnglish, and the alignment is achieved by the following
steps, using a Japanese parser, an English parser, and abilingual dictionary.
2.1 Dependency Analysis of Sentences
Japanese sentences are converted into dependency structures using the morphological analyzer, JUMAN (Kurohashi et al., 1994), and the dependency analyzer, KNP(Kurohashi and Nagao, 1994). Japanese dependency structure consists of nodes which correspond to content words.
Function words such as post-positions, affixes, and auxiliary verbs are included in the nodes.
For English sentences, Charniak’s nlparser is used toconvert them into phrase structures (Charniak and Johnson, 2005), and then they are transformed into dependency
structures by rules defining head words for phrases. In thesame way as Japanese, each node in this dependency tree
consists of a content word and related function words.
Figure 1 shows an example of tree structure. The root ofa tree is placed at the extreme left and phrases are placed
from top to bottom.

ᣣᧄ 䈪

2.2 Detection of Word/Phrase Correspondence
Candidates

新宿 → Shinjuku ↔ Shinjuku (similarity:1.0)
ローズワイン → rosuwain ↔ rose wine (similarity:0.78)

In Figure 1, the correspondence candidates “ 日本 (Japan)↔ Japan”, “請求 (claim) ↔ claim”, “申し立て (allegation)↔ file / claim”, and combination of “ 保険 (insurance) ↔
insurance” are found.
2.3 Selection of Correspondence Candidates
Results of previous procedure may contain ambiguous orincorrect correspondence candidates.
In Figure 1, for example, Japanese word “ 保険 (insurance)” and English word “insurance” occurs twice for eachsentence, so there happens ambiguity. Moreover, “ 申し立て (allegation)” has two possible translations, “file” and“claim” in the English sentence. In addition, unambiguous
but incorrect correspondence candidates might be sometimes detected. Thus, we need to select plausible correspondences among correspondence candidates.
In order to construct consistent over all alignment, we
define consistency score for a pair of correspondences (introduced in Section 3). Then, we select the best set of correspondences in which a summation of consistency scoresof all the combinations of correspondences is maximum.
nnalignment i=1 j=i+1

consistencyscore(ai , aj )will have to file଻㒾

Correspondence candidates between Japanese word/phrase
and English word/phrase are detected by a JapaneseEnglish dictionary.
At this moment, the dictionary is not probabilistic. By
looking up the whole pair of Japanese words and English
words in the dictionary, correspondence candidates are detected deterministically.
In addition to the dictionary, we also handle transliteration. For possible person names and place names suggested by the morphological analyzer and Katakana words(Katakana is a Japanese alphabet usually used for loan
words), their possible transliterations are produced andtheir similarity with words in the English sentence is calculated based on the edit distance. If there are similar wordpairs whose edit distance exceeds a threshold, they are handled as a correspondence candidate.
For example, the following words can be considered ascorrespondence by the transliteration module, which cannot
be handled by the existing bilingual dictionary entries:

argmaxyou(in Japan)(1)

where ai and aj are one of correspondences.
2.4 Handling of Remaining Words
The alignment procedure so far finds some correspondences in parallel sentences. Then, we merge the remaining
nodes into existing correspondences.
First, the root nodes of the dependency trees are handled
as follows. In the given training data, we suppose that all(insurance)ળ␠ 䈮 ኻ䈚䈩

insurance(to company)

an claim଻㒾(insurance)⺧᳞ 䈱

insurance(claim)↳䈚┙䈩 䈏

with the office

1/2 + 1/1 = 1.5(instance)น⢻䈪䈜 䉋

in Japan(you can)Figure 1: Example of ambiguities.
੤Ꮕ
ὐ䈪䇮
⓭ὼ
䈅䈱
ゞ䈏
㘧䈶಴䈚䈩 ᧪䈢 䈱䈪䈜 䇯

the carcameat mefrom the sideat the intersection

Figure 2: Example of extension.

parallel sentences have appropriate translation relation. Accordingly, if neither of the root nodes (of the Japanese dependency tree and the English dependency tree) is included
in any correspondences, the new correspondence between
the two root nodes is generated. If either root node is remaining, it is merged into the correspondence of the otherroot node.
Then, for both Japanese remaining node and English remaining node, if it is within a base NP and another node in
the NP is in a correspondence, it is merged into the correspondence. At last, other remaining nodes are merged into
correspondences of their parent (or ancestor) nodes.
In an example shown in Figure 2, “ あの (that)” is merged
into the correspondence “ 車 (car) ↔ the car”, since it iswithin an NP. “突然 (suddenly)”, “at me” and “from theside” are merged into their parent correspondence, “ 飛び出して来たのです (rush out) ↔ came”.

3 Structural Phrase Alignment Based on
Consistency Criteria
3.1 Consistency of Alignment
Before introducing our proposing method, let us think of
the word “consistency” itself. In Figure 3, the triangles
represent the abstract of tree structure of each language,
and the lines represent the correspondences. Among manylines, one line (on which a cross is placed) seems to be
strange, which means disturbing the conformity of wholealignment.
This instability is apparent in visual. To measure the instability quantitatively, we focus on the “distance” in eachlanguage tree structure between two lines. In the example,
although the distance between two lines in source language
is far, the distance that in target language is near. Since thetree structure is constructed based on dependency information, such case rarely happens. In other words, it is impossi-

Near!
Far!

Figure 3: Example of Consistency.

ble that the two corresponding phrase pairs are semanticallyclose in target language, but they are far in source language.
Therefore, suitably capturing the distance in each tree
structure of all the pairs of lines leads to the overall consistent alignment of the parallel sentence. For this purpose,
we introduce “consistency score”, and this is explained in
the following sections.
3.2 Consistency Score
To obtain consistent alignment within a sentence, we definea consistency score based on the dependency analysis tree.
First, we focus on an arbitrary pair of correspondences
ai as (pSi , pT i ) and aj as (pSj , pT j ), where pSi represents
the phrase of a i in source language and p T i represents thephrase of ai in target language. It is same for p Sj and pT j .
Then, the dependency distance of source languagedS (ai , aj ) is defined as the distance between p Si and pSj ,and the dependency distance of target language d T (ai , aj )is defined as the distance between p T i and pT j . Then, theconsistency score is defined as follows:

tree, we give a score f (d S , dT ) = 1/dS (aamb , aunamb ) +1/dT (aamb , aunamb ) to the correspondence a amb andaunamb .
Then, we hold an assumption that the closer a unamb is
to aamb , the more strongly a unamb supports aamb . Consequently, we accept the ambiguous correspondence withthe highest score and reject the others conflicting with theaccepted one. This calculation is repeated until all the ambiguous correspondences are resolved.
For example, in Figure 1, considering only one determined correspondences “ 日本 (Japan) ↔ Japan” as a clue,
the scores are calculated, and the correspondence “ 申し 立て (allegation) ↔ file” with the highest score is adopted.
At the same time, the conflicting correspondence “ 申し 立て (allegation) ↔ claim” is rejected. After that, the correspondence “ 請求 (claim) ↔ claim” is unambiguous, so it isadopted.
3.4 Proposed Model
Here we would like to refine the heuristic definition of distance and distance-score function. We proposed two models.

3.3 Baseline Method

Proposed Model 1
First, we refine distance-score function in order to reject unambiguous and incorrect correspondences. Here, thedefinition of dS (ai , aj ) and dT (ai , aj ) is the same as thebaseline model.
Using the gold standard alignment data from
NICT(Uchimoto et al., 2004), which includes about
40,000 sentence pairs, we learned the frequency distribution of distance pair. Figure 4 shows the result of automaticlearning form gold standard data.
Based on the observation of gold standard data, we design f (dS , dT ) as follows:
Criteria 1: f (dS , dT ) is positive if both dS and dT aresmall, which means the relation between the two correspondences is appropriate;
Criteria 2: f (dS , dT ) is 0 if both dS and dT are large,
for the relation is not so important if they are far from eachother;
Criteria 3: f (dS , dT ) is negative is dS is large but dT
is small, or dT is large but dS is small, which means therelation between the two correspondences is inappropriate.
From these assumptions, the function f (d S , dT ) is modified like the graph in Figure 5. The modification is done
satisfying the assumptions and covering the learning result.

In the baseline method, all the unambiguous correspondences are adopted without any constraint. For the ambiguous correspondences, we use distance and distance-scorefunction.
Here, all the branches are treated as same: the distance is 1 for all. This means that we define the distance between correspondences as the number of traversing nodes in a dependency tree.
Furthermore, thedistance-score function is also simple. Suppose there isa correspondence a amb with ambiguity, and there is an
unambiguous correspondence a unamb with the distancedS (aamb , aunamb ) in the Japanese dependency tree andthe distance dT (aamb , aunamb ) in the English dependency

Proposed Model 2
Japanese dependency analyzer KNP outputs dependency
type information for each phrase, and Charniak’s nlparser
also outputs tag information. According to such information, we define dependency type score ts S (pSi ) to show
the strength of segmentation for source language between
pSi and the phrase p Si depends on, and define ts T (pT i ) fortarget language. This definition is set by hand and Figure 6
shows part of them. An example of dependency type scoreis shown in Figure 7, where the label placed on each branch
represents dependency type and the number placed over thelabel represents dependency type score.

consistencyscore(ai , aj ) = f (dS (ai , aj ), dT (ai , aj ))It is referred as f (dS , dT ) for short. f (dS , dT ) is a function that maps a pair of distance to the score. Function d S ,dT and f are defined differently in different model.
The consistency of whole alignment is defined as a summation of the consistency scores of all the pairs of correspondences (as shown in Equation 1).
Correct correspondences are supported by their neighborcorrespondences, or the distance between them is small inboth sides. Such relations produce good scores and contribute to the alignment consistency.

12
10
8
6
4
2
0

12
10
8
6
4
2
0
141210864

2 0 0 2 4 6 8 10 12 14

0

2

4

6

8 10 12 14

14
12
810
0246

Figure 4: Learned Distance Pair Distribution for Proposed Model 1.
predicate:level C
predicate:level B+ / B
predicate:レベル B- / A
to casewo case / ni case / de casega case / no case / adnominal
inside bunsetu
predicate:level A+

6
5
4
3
2
1

S/SBAR/SA/:
VP/ADVP/ADJP
WHADVP/WHADJPNP/PP/INTJ
QP/PRT/PRN
others5
4
32

Figure 6: An Example of Dependency Type Distance.
Then the dependency distance d S (ai , aj ) and dT (ai , aj )are defined as:
dS (ai , aj ) = dStype (ai , aj )

6
4
2
0
-2
-4
-6

dT (ai , aj ) = dT type (ai , aj )

14
12
108
64
20
0

4

2

6

8 10 12 14

Figure 5: Smoothed Distance-Score Function for Proposed
Model 1.

䋳䊂ᩰ(in Japan)

䋱
ᢥ▵ౝ
䋱
ㅪ↪

䋳
NP

ᣣᧄ 䈪

[case “de”](insurance)䋱NNળ␠ 䈮 ኻ䈚䈩(to company)[renyou]䋱[inside clause] ᢥ▵ౝ
䋲
䊉ᩰ
䋲
䉧ᩰ

will have to file଻㒾

[inside clause]↳䈚┙䈩 䈏

[case “ga”]น⢻䈪䈜 䉋

଻㒾䋳NP(insurance)(instance)(claim)insurance

an claim
䋱
NN

⺧᳞ 䈱

[case “no”]

you

insurance

䋳PPwith the office䋳PP

in Japan(you can)Figure 7: Example of Dependency Type Distance.

where dStype (ai , aj ) is defined as the dependency typedistance between correspondence a i and correspondence a j
in source language and d T type (ai , aj ) is defined as the dependency type distance in target language.
The detailed definition of d Stype (ai , aj ) anddT type (ai , aj ) are as follows, where a i = (pSi , pT i )and aj = (pSj , pT j ):If phrases pSi and pSj are located in the same path ofthe dependency tree, then d Stype (ai , aj ) is defined as theassumption of ts S (pSi ) where pSi is the node existing in
the path between p Si and pSj . dT type (ai , aj ) is defined in
the same way.
If phrases pSi and pSj belong to different subtrees, thendStype (ai , aj ) is defined as the assumption of ts S (pSx )where pSx is the node existing in the path between p Si andthe root node plus the assumption of ts S (pSx ) where pSx
is the node existing in the path between p Sj and the root
node. dT type (ai , aj ) is defined in the same way.
For example, in Figure 8, the distance between the connected two correspondences is (d S , dT ) = (1, 1), and they
are very close both in Japanese and English, so the scoreis plus. On the other hand, in Figure 9, the distance is(dS , dT ) = (1, 7). They are close in Japanese, but far in
English, so the score is minus. This is because their relation may be inappropriate.
Figure 10 shows the function f (d S , dT ). The dots are
the result of automatic learning form gold standard data,䋳䊂ᩰ(in Japan)

䋱
ᢥ▵ౝ
䋱
ㅪ↪

䋳
NP

ᣣᧄ 䈪

[case “de”](insurance)䋱NNળ␠ 䈮 ኻ䈚䈩(to company)[renyou]䋱[inside clause] ᢥ▵ౝ
䋲
䊉ᩰ
䋲
䉧ᩰ

will have to file଻㒾

[inside clause]

䋳
NP

଻㒾
(insurance)⺧᳞ 䈱[case “no”](claim)(instance)น⢻䈪䈜 䉋

insurance

an claim
䋱
NN

Distance=(1,1)
Score Plus↳䈚┙䈩 䈏

[case “ga”]

youinsurance

䋳PPwith the office䋳PP

in Japan(you can)Smoothing
Learned

6
4
2
0
-2
-4
-6
0

5

10

20
15
10
20 05

15

Figure 8: Example of Good Relation.
䋳䊂ᩰ(in Japan)

䋱
ᢥ▵ౝ
䋱
ㅪ↪

䋳
NP

ᣣᧄ 䈪

[case “de”](insurance)(to company)

䋱[inside clause] ᢥ▵ౝ
䋲
䊉ᩰ
䋲
䉧ᩰ

䋱
NN

ળ␠ 䈮 ኻ䈚䈩[renyou]䋳NP଻㒾(insurance)⺧᳞ 䈱[case “no”](claim)Distance=(1,7)
Score Minus↳䈚┙䈩 䈏

[case “ga”]

Figure 10: Distance-Score Function for Proposed Model 2.

will have to file଻㒾

[inside clause]you(instance)น⢻䈪䈜 䉋

insurance

an claim
䋱NN

insurance

䋳PPwith the office䋳PP

in Japan(you can)Figure 9: Example of Bad Relation.

and the meshed graph is manually modified function. The
modification is done in the same manner as model 1.
3.5 Alignment Consistency Score
The consistency of alignment as a whole is denoted as asummation of f (d S , dT ) of all the combinations of correspondences. The best alignment is acquired by selecting
correspondences to make the summation the greatest. However, it is not reasonable to expand all the cases and checkthem all since it easily falls into the combinatorial explosion. Therefore, the best alignment is searched greedily.
Focusing on an arbitrary correspondence, calculate thedistance and the score between focusing correspondence
and all the other correspondences. The score of the focusing correspondence is defined as a summation of the calculated scores. For all the other correspondences, the scoreof the correspondence is calculated in the same manner.
The highest scored correspondence is regarded as a correct one and adopted. At the same time, negatively scoredcorrespondences which exceed the negative threshold are
rejected.
These steps are iterated until all the correspondences are
adopted or rejected, and the approximately best alignmentis acquired.

4 Experimental Results and Discussions
4.1 Alignment Experiment
We selected randomly 500 sentences of the newspaper domain corpus from NICT and there are also gold standardalignment data(Uchimoto et al., 2004). The unit of English
sentence is a word, but the unit for Japanese sentence isdifferent between our output and gold standard (althoughboth ours and gold standard are phrase-base, the criterionof phrase is different). Therefore, the evaluation was doneon character base for Japanese.
Character base evaluation should have a problem causedby the length of words. In the case of that, long words are
misaligned, the accuracy get worse heavily. However, correctly aligned long words give good effect on the accuracy
vice versa. Thus, it doesn’t seem to be a big problem.
We used two bilingual dictionaries.
One isKENKYUSYA’s Japanese-English dictionary, consisting of 36K entries, and we extracted 214K one-to-one
translations.
The other is KENKYUSYA’s EnglishJapanese dictionary, consisting of 50K entries, and we
extracted 303K one-to-one translations.
The evaluation criterion is basic precision, recall, and Fmeasure. The gold standard data is annotated with only
sure (S) alignments (no possible (P ) alignments (Och andNey, 2003a)). Figure 11 shows an example of evaluation.
The black colored cells represent the gold standard alignment, and white boxes represent our output. Precision is an
accuracy of the output, in the example, the precision is 9 /12 = 75%. Recall is a coverage of the gold standard, in theexample, the precision is 9 / 11 = 82%. The F-measure isa harmonic mean of Precision and Recall, and it is definedas:
F − measure =1
1
1
2∗P recision + 2∗Recall

In the examle, F-measure is 78 points.
The evaluation results are shown in Table 1. “Baseline”
is the heuristic method introduced in Section 3.3. “Model1” is one of the proposed methods in which the distancescore function is refined. “Model 2” is a method using dependency type distance.
For comparison, we segmented the data using the morphological analyzer JUMAN (Kurohashi et al., 1994) andcreated alignments using the simple and standard statisticalalignment tool GIZA++ (Och and Ney, 2003b). 10 iterations of each of the IBM model 1, 2, 3 and 4 were used forstatistical alignment.
In Table 1, we can see about 2.0 points improvement be-E1

Table 1: Alignment Evaluation Results.
Precision Recall F-measure
Baseline
65.6
65.1
58.8
Model 1
67.4
68.0
60.8
68.6
68.5
61.9
Model 2
GIZA++
59.9
17.0
26.4

E2
E3
E4
E5

ᵠᵿᶑᶃᶊᶇᶌᶃ

E6ᵫᶍᶂᶃᶊᴾᵐ

E7
E8
E9
J1

J2

J3

J4

J5

J6

J7

J8

J9

Figure 11: Alignment Evaluation Example.

tween Baseline and Model 1. This shows that the refiningof distance-score function has great effect on the alignment
accuracy. Moreover, we can say dependency type distancealso works well from the improvement between Model 1
and Model 2.
At present, the distance-score function is defined by handwith the clue of automatically learned data, but we have notdone any tuning to the function. In addition, the threshold of rejecting the bad-scored correspondence candidate
also needs to be tuned. It is also important to reconsider
the procedure of calculating consistency score. Throughthese modifications, the accuracy is expected to be more
improved.
The dependency type score is also defined manually now.
Since the number of types are not so large, it is not so hardto define by hand. However, there is no confidence in the
degree of the score. There may seem to be several ways
to learn the strength of the segmentation: learn from themonolingual corpus, or use parallel corpus, and so on. We
need to take some of them and try to learn automatically.
The F-measure result of GIZA++ is extremely bad. Precision is not so different between GIZA++ and ours, but the
Recall is very low. This is because, as it is often said, thestatistical methods work well for language pairs that are notso different in the point of language structure (e.g., English
and Spanish). Japanese and English have significantly different structure. Most famous difference is that Japanese
sentences consist of SOV word order, but English word order is SVO. For such language pair as Japanese and English,
deeper sentence analysis using NLP resources is necessary,
like our method.
Sample alignments are shown in Figure 12 and Figure
13. Wrongly aligned parts in the baseline method are modified in the model 2.
4.2 Translation Experiment
We also conducted translation experiment. For this purpose, we utilized around 218K parallel sentences for train-

Figure 12: Sample Alignment 1.

ing, and 500 sentences for testing. All the sentences areon newspaper domain(Utiyama and Isahara, 2003). The
translation results are summarized in Table 2 and Table 3.
Results were evaluated by n-gram precision based metrics,
BLEU and NIST, with only one reference. We show 3, 4,
5-gram evaluation results in the tables.
From the result, it is able to be said that the translation
quality is improved by our proposed method. The improvement of alignment accuracy leads to the improvement of
the quality of translation examples used in translation step.
Sample translations are shown in Table 4. The numerals following the method name represent the 4-gram BLEU
score of the output.

5

Conclusion

We have proposed a new alignment method using distancescore function f (d S , dT ) and dependency type distancedtype (ai , aj ) to improve structural phrase alignment. We
defined the overall alignment consistency as a summationof f (dS , dT ) of all the pairs of correspondences. Our new
method can evaluate alignment as a whole, and eliminate
inappropriate correspondences, which baseline method was
not able to eliminate. With the new method, we succeed to
improve structural phrase alignment and achieved 35 points
higher alignment accuracy than simple statistical method,and 3 points than baseline.
What we need to do in the future is to find the dependency type distance by machine learning, which is set byhand at present. There are several ways to learn: using parallel corpus or huge size single language corpus.
Our proposed method utilize the advantage of tree structure and highly rely on the information. This is very usefulas shown in experiments, but it is also easy to cause alignment errors derived from the parsing errors. For Japanese,even thought the parsing accuracy is basically high, itReference
Baseline (35.2)
Model 2 (48.6)
ReferenceBaseline (0.0)
Model 2 (28.2)

Table 4: Sample Translations.
under the revised foreign exchange and foreign trade law the government can restrictor suspend remittances to and trade with north korea
first of all it will serve restrictions and halting remittances to and to trade with northkorea the foreign exchange and foreign trade law
one is that restrictions and halting remittances to and trade with north korea the foreignexchange and foreign trade law
the dependence on the united states for security has allowed the country to devote allits energy to economic development
great relying on the us arsenal as the united states security security has allowed economic in to devote himself to japan
great its dependence on the united states security security has allowed economic in to
devote himself to japan

Table 2: Translation Evaluation Results (BLEU).
3-gram 4-gram 5-gram
Baseline
8.19
5.05
3.38
Model 2
8.64
5.40
3.59

Table 3: Translation Evaluation Results (NIST).
3-gram 4-gram 5-gram
Baseline 2.6993 2.7003 2.7006
Model 2 2.7063 2.7073 2.7077

ᵠᵿᶑᶃᶊᶇᶌᶃ

Eugene Charniak and Mark Johnson. 2005. Coarse-to-finen-best parsing and maxent discriminative reranking. In
Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL’05), pages 173–
180, Ann Arbor, Michigan, June.
Daniel Gildea. 2003. Loosely tree-based alignment formachine translation. In Proceedings of the 41st Annual
Meeting on Association for Computational Linguistics,
pages 80–87.

ᵫᶍᶂᶃᶊᴾᵐ

Declan Groves, Mary Hearne, and Andy Way. 2004. Robust sub-sentential alignment of phrase-structure trees.
In Proceedings of the 20th International Conference on
Computational Linguistics, pages 1072–1078.
Figure 13: Sample Alignment 2.

sometimes fails to parse the sentence and outputs wrong
tree structure. For English, generally said to be difficult tocorrectly parse, the condition is rather tragic.
Therefore, we need some strategies which can modify
the tree structure itself. This is able to be done by using ourconsistency criteria.

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